
Continual learning setting aims to learn a single model which can incrementally accumulate knowledge across multiple tasks, without forgetting. Main-stream efforts in Continual Leaning [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [2]}, [6]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]} assume that the data which is introduced in each incremental task is fully annotated. Efforts in Novel Class Discovery can help to relax this requirement, where the model could be tasked to identify classes from the instances of a new task automatically, based on the learnings that it already had. Then, these identified novel categories may be incrementally learned.
We hope that the unification of these two streams of research would lead to a more pragmatic problem setting by building on their complementary characteristics.
